Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

ABSTRACT:

Background

Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images.

Methods

We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PV

SUBMITTER: Azimi MS 

PROVIDER: S-EPMC10963814 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature